545results about How to "Efficient acquisition" patented technology

The invention discloses a method for automatically generating a BIM model based on point cloud data. The method comprises the following steps: step 1, preprocessing original point cloud data; 2, carrying out the semantic segmentation of the point cloud of the building construction; step 3, segmenting point cloud instances of the building components; 4, performing IFC construction reverse Revit modeling based on the point cloud data and the point cloud instance segmentation result, and converting the point cloud data into a three-dimensional BIM model of the building component; 5, after reverse modeling of the building components is completed, all the building components are recombined and integrated based on the coordinate data, a complete three-dimensional BIM building model is formed, accurate positions and relations of all the building components in the global coordinate system are ensured, and overall optimization of the BIM building model is conducted. The invention relates to the technical field of information, and can solve the problems that in the prior art, modeling based on point cloud data depends on manual stroking and mold turning, the efficiency is low, and errors are large.

The invention relates to the technical field of geological ecological monitoring, in particular to a geological ecological monitoring system and method. The method comprises the following steps: acquiring a topographic map of a monitoring area; performing real-time data acquisition by using a sensor network according to the topographic map of the monitored area, and constructing a rock-soil physical characteristic matrix to obtain the rock-soil physical characteristic matrix; performing rock-soil mechanical response spectrum construction according to the rock-soil physical characteristic matrix to obtain a rock-soil mechanical response spectrum; according to the monitored area topographic map, point cloud data processing is utilized to construct a side slope geometric model, and a side slope three-dimensional geometric model is obtained; performing unit stress analysis according to the three-dimensional geometric model of the side slope to obtain unit stress data; and performing strength reduction calculation according to the unit stress data to obtain a reduction strength parameter. The geological ecological system is comprehensively, dynamically and systematically monitored and evaluated, so that geological disaster prevention and control and ecological environment protection are better served.

The invention relates to a high-voltage cable intelligent inspection method and system based on multimode identification, and the method comprises the steps: deploying an intelligent identifier and cable monitoring equipment according to the equipment characteristics and environmental conditions in a high-voltage cable line; reading identification data of the intelligent identification through a double-frequency reading device, and collecting cable state monitoring data through a cable monitoring device; the information is uploaded to the Internet of Things cloud platform; carrying out fusion analysis on the received data in the cloud platform, evaluating the health condition of the cable in combination with a historical record and a fault recognition model, and generating an inspection result; and displaying the inspection result through the intelligent monitoring center, calculating the risk score of each device according to the identification data, the cable state monitoring data and the fault risk prediction result, and dynamically adjusting the optimal inspection route and the inspection frequency. Compared with the prior art, unique identification and multi-dimensional information storage of the cable and accessory equipment of the cable are achieved, and the fineness and comprehensiveness of cable operation state monitoring are improved.

An elevator fault diagnosis digital twinning system building method belongs to the field of digital twinning and elevator fault diagnosis, and comprises the following steps: 1, building an elevator digital twinning fault diagnosis four-dimensional model framework; secondly, elevator data are collected; thirdly, a virtual elevator model is constructed; fourthly, a numerical model of the elevator in the vertical direction is constructed; 5, improving the PINNs model, introducing a parameter into the loss function as a physical information regulator, and introducing a reactivation algorithm to optimize the loss function; sixthly, simulated vibration data are obtained, a small number of collected elevator fault vibration signals are input into the PINNs model with motion equation constraints, and simulated elevator vibration data are obtained; 7, the improved GCN model is used for elevator fault diagnosis; and 8, establishing an elevator fault diagnosis digital twin platform. The elevator fault diagnosis and the digital twinning technology are fused, and the fault diagnosis accuracy is effectively improved.

The invention provides a myocardial infarction positioning method based on cross-modal knowledge fusion. The method comprises the following steps: acquiring a target 1D-VCG signal and constructing a 2D-VCG image data set; extracting spatial features, time sequence features and pseudo tags from the 2D-VCG signals and the 1D-VCG signals through a teacher model and a student model; aligning and transforming the spatial features and the time sequence features through a cross-modal generator; and cross-modal knowledge fusion and MI positioning are realized through a cross-modal generator. According to the method, through knowledge enhancement and student enhancement strategies, the accuracy of myocardial infarction positioning is improved, and the overall framework of cross-modal learning is optimized through the adaptive weight method; the method is realized in a knowledge distillation form, spatial features of 2D-VCG are successfully fused into 1D-VCG time sequence analysis, and the positioning accuracy of myocardial infarction is remarkably improved by combining cooperative work of a teacher model, a student model and a cross-modal generator.

The invention provides a semiconductor equipment data acquisition method and a semiconductor equipment data acquisition system supporting standardized communication transformation, which are used for realizing data acquisition and process control on different types of semiconductor equipment, formulating corresponding communication rules for equipment conforming to a communication standard, and converting an equipment communication protocol by a cluster system so as to match a standard interface. For equipment which does not completely meet the standard but can realize communication through programming, the trunking system directly acquires required data and converts the required data to meet the standard interface specification. For equipment without communication capability, data is acquired through an equipment interface by adopting an image recognition technology, data input and function operation are realized through simulating keyboard and mouse operation, and finally, protocol conversion from data to a standard interface is completed by a cluster equipment control system. And the equipment data is converted into a message data packet conforming to a standard semiconductor equipment communication interface, so that docking with a semiconductor equipment data analysis and process control system is realized, and the production efficiency and quality of the semiconductor equipment are improved.

The invention discloses an equipment maintenance knowledge representation method combining knowledge distillation and semantic analysis. The method comprises the steps of collecting structured data, unstructured text data and multi-modal data, constructing a semantic knowledge graph by using a natural language processing technology, extracting visual knowledge by using a computer vision technology, and fusing the visual knowledge with semantic knowledge to form a multi-modal knowledge base. And performing interactive knowledge distillation on the text data by using a large language model, generating optimized knowledge key points, and fusing the optimized knowledge key points with the multi-modal knowledge base to generate a comprehensive knowledge base. Knowledge is hierarchically organized according to the domain ontology and stored in a graph database, a natural language query interface is constructed, intelligent answering is performed by integrating technologies, question and answer corpus training is automatically generated, and automatic question and answer services are provided for users. According to the method, heterogeneous data integration, multi-modal knowledge fusion, hierarchical knowledge organization, natural language interaction and man-machine collaborative optimization are realized, and an intelligent equipment maintenance knowledge base can be efficiently constructed.

The invention discloses a network resource optimization method for network slices, which comprises the following steps of: acquiring historical flow data, user behavior data and network topology information of each network slice from each network node in real time by utilizing a distributed data acquisition system, and performing data preprocessing on the historical flow data, the user behavior data and the network topology information to form a feature data set; inputting the feature data set into a prediction model constructed based on an attention mechanism and a mixed density network algorithm, optimizing the network weight through a gradient descent algorithm, and training the prediction model; inputting feature data of the current moment into the trained prediction model, and outputting prediction values of probability distribution of different network slices for future traffic demands; and formulating a resource pre-allocation strategy according to the predicted probability distribution of different network slices for future traffic demands, and dynamically optimizing and adjusting network resources. According to the invention, through accurate resource pre-allocation, network resources are utilized to the maximum extent, the operation cost is reduced, and the resource utilization efficiency is improved.

The invention discloses a line equipment defect labeling method based on an unmanned aerial vehicle power line image, and relates to the technical field of defect labeling, and the method comprises the following steps: obtaining a historical picture of a power transmission line, and carrying out the preprocessing of the historical picture; performing feature extraction on the preprocessed historical picture by using an image segmentation technology to obtain a line image feature value; manually marking defect pictures in the historical pictures to obtain the defect pictures, and marking the line image feature values of the defect pictures as defect image feature values; and taking the defect image feature value and the line image feature value as a training set, and performing model training by using a convolutional neural network to obtain a power line defect identification model. According to the defect identification algorithm, various defects of the power line equipment can be accurately detected and labeled, and the detection rate and accuracy of equipment faults are improved.

The invention discloses an unmanned aerial vehicle target tracking method based on feature fusion and sparse attention, and the method comprises the following steps: selecting improved Res2NeXt to obtain multi-scale features in order to enable a tracker to directly pay attention to an important space region and avoid the increase of the calculation amount, and carrying out the recognition of the multi-scale features through the improved Res2NeXt. Channel interdependence and space interdependence of target features are learned, self-adaption fusion is carried out, and feature representation is enhanced. A self-context enhancement module and a cross feature module are used, so that useful semantic context information can be adaptively focused to perform feature enhancement, and feature maps of each branch can be accepted to perform fusion. And introducing a regression and classification model as a prediction head to predict a target template. The method can solve the problems of target shielding and small target of the unmanned aerial vehicle, improves the local feature processing capability of the model, and effectively improves the accuracy of a tracking algorithm.

The invention discloses a joint beam forming optimization method for an RIS auxiliary low-altitude communication system. The problem of performance bottleneck caused by interaction of active beam forming and passive beam forming is solved by adopting a genetic algorithm based on a dynamic adaptive theory. Aiming at the limitation of a standard optimization method, a hybrid strategy of genetic algorithm optimization and a full-connection neural network is explored, and intelligent collaborative setting of system parameters is realized. Simulation results show that the hybrid algorithm not only simplifies the calculation process to obtain the optimal network parameters, but also effectively optimizes the system structure by dynamically adjusting the RIS reflection configuration. On the basis, the signal transmission quality is improved, interference is reduced, and it is guaranteed that the RIS-assisted low-altitude unmanned aerial vehicle intelligent communication system stably and efficiently operates in a complex wireless transmission environment.

The invention relates to a poplar property high-throughput accurate quantitative identification method, system, equipment and medium, the innovation of the method lies in that spectral data analysis and automatic script processing are combined, efficient data acquisition, import, matching and storage are realized, abnormal point detection is carried out by introducing principal component analysis and anti-noise mahalanobis distance, a data set is optimized, and the identification accuracy of the poplar property is improved. Building and training a model to realize high-flux accurate prediction of the material property and the density; a sample control module, an acquisition test module, an automatic processing module, a data set determination module and a result output module are integrated on the system, so that full-process automatic and intelligent management is realized; the equipment comprises a database and a memory, and the method is cooperatively executed through the database and the memory to ensure efficient operation of the method; a computer readable medium stores executable instructions, which is convenient for wide deployment and application. The method is excellent in high-throughput analysis, cost saving, environmental protection and data accuracy, and development of the poplar property identification technology is effectively promoted.

The invention relates to the technical field of biological detection, and discloses a method for detecting a fusion gene in high-throughput sequencing, which comprises the following steps: S1, obtaining sequencing data and performing quality control to obtain filtered sequencing data; s2, comparing the high-quality sequencing data with a reference genome to obtain a comparison file; s3, screening abnormal alignment sequences according to the alignment file, and obtaining fusion breakpoint sequence fragments and candidate fusion genes; s4, performing statistics on fusion information according to the candidate fusion genes; s5, screening fusion events to obtain potential fusion gene pairs; s6, calculating the fusion confidence coefficient of the fusion gene pair; s7, judging whether the fusion gene is a true positive fusion gene or not according to the fusion confidence, and outputting a result; according to the method, the abnormal alignment sequence is screened and filtered according to the aligned parameter information, and the fusion gene pair with higher confidence is obtained by calculating the fusion confidence, so that the detection rate and correctness of the fusion gene can be effectively improved.

The invention provides a method for characterizing the heterogeneity of a millimeter-level shale oil and gas reservoir. The method mainly solves the problem that the reliability and accuracy of a reservoir evaluation result of an existing volume sampling method are low. The method is characterized by comprising the following steps that S1, shale millimeter-level mineral components are recognized and drilled; s2, based on the obtained powdery sample, oil-containing and non-oil-containing tests are carried out; determining occurrence pore size distribution to carry out reservoir characterization through comparison before and after pumping; s3, on the basis of the obtained powdery sample, carrying out oiliness characterization on different lamina / layered components; and S4, carrying out millimeter-level shale oil reservoir heterogeneity characterization on the basis of the reservoir property and oiliness results of the different strata / layered components. According to the method provided by the invention, the identification precision of the shale formation / stratified structure can be enhanced, millimeter-level fine representation of lithology of different formation / strata is realized, and favorable support is provided for shale oil enrichment mechanism, sweet spot prediction and the like.

An astronavigation material database system belongs to the technical field of astronavigation material databases and comprises a basic platform layer, an application service layer and a presentation layer. The public service layer comprises a public service plate; the application function layer comprises but is not limited to a material management plate, a material data application plate and a material big data analysis plate. The invention discloses an astronavigation material data system, which comprises a word standard specification, establishes an astronavigation material database, can perform big data analysis according to a computer software technology, a network technology and a digital technology, and can realize intelligent recommendation and intelligent knowledge management of astronavigation materials.

The invention relates to the technical field of wetland ecosystem degradation identification, in particular to a wetland ecosystem degradation identification method and system. According to the technical scheme, the method comprises the following steps that a comprehensive wetland biodiversity monitoring system is constructed, and multiple types of sensors and high-definition cameras are deployed in a wetland so as to obtain multi-source data related to wetland organisms and environments; preprocessing the obtained original data to enable the original data to be suitable for subsequent artificial intelligence analysis; and biological species identification and biological diversity evaluation are respectively carried out by using an image identification technology based on deep learning and a machine learning algorithm. According to the method, the foundation is laid for analysis by comprehensively and accurately collecting wetland biological and environmental multi-source data and an effective preprocessing method; according to the method, biological species are accurately identified, diversity parameters are evaluated, indexes are reasonably set, trends are reliably analyzed by using a moving average method, data are periodically updated, model parameters and structures are adjusted, wetland changes are self-adapted, and wetland ecology is effectively monitored and evaluated.

The invention discloses a pose estimation method, computer equipment and a storage medium. The method comprises the following steps: acquiring point cloud data of a current frame, and performing feature point extraction on the point cloud data to obtain at least two types of feature points; performing region division on the feature points of each category to obtain at least two categories of feature points corresponding to each region; clustering processing is carried out on the feature points, corresponding to the areas, of the categories to obtain centroid point sets corresponding to at least two categories; and performing pose estimation by using the centroid point sets corresponding to the at least two categories to obtain the current pose of the target equipment. According to the scheme, the accuracy of pose estimation can be improved.

The invention provides a business intelligent report generation method and system based on a large model and a storage medium, and the method comprises the steps: obtaining a user report demand, inputting the user report demand into a pre-trained large model, and carrying out the intention recognition, and obtaining a user intention; determining an SQL query statement according to the user intention, and performing data acquisition according to the SQL query statement to obtain target data; and obtaining a report intention in the user intention, determining a target report according to the report intention, and performing data filling on the target report according to the target data to obtain a business intelligent report. According to the embodiment of the invention, the automatic SQL query and commercial intelligent report generation process effectively reduces the generation time of the commercial intelligent report and improves the generation efficiency of the commercial intelligent report.

The invention relates to the technical field of ultrasonic measurement, in particular to a heart parameter measuring method and system. According to the heart parameter measurement method, the position of the heart is acquired, and the probe is finely positioned according to the position of the heart, so that the probe can accurately and quickly find and acquire the ultrasonic image at the standard section of the heart; then capturing a target standard section in the ultrasonic image by adopting a pre-trained self-supervised deep learning model; and finally, carrying out key frame extraction on the 2D echocardiogram video so as to measure a heart structure in a key frame to obtain heart structure parameters, cutting out a region of interest in the Doppler echocardiogram, and carrying out segmentation processing on the region of interest to obtain functional parameters of the heart. By means of the mode, various parameters of the heart can be accurately and efficiently obtained, full automation is achieved in the whole process, and the accuracy and consistency of various parameter test results of the heart are guaranteed.

The invention provides a coal rock impact tendency testing and evaluating method based on a dynamic strain gauge, which comprises the following steps: pretreating a coal rock structure standard test piece, performing a uniaxial compression test on the coal rock structure standard test piece, and acquiring a preset index test result of the coal rock structure standard test piece; constructing a model for evaluating the impact tendency of the coal rock structure based on a BP neural network algorithm; and taking the preset index test result as input, and obtaining the impact tendency grade of the to-be-tested coal rock structure test piece by using a BP neural network-based coal rock structure test piece impact tendency evaluation model. The invention further provides a coal rock impact tendency testing and evaluating system based on the dynamic strain gauge. According to the method, the accuracy and reliability of the impact tendency of the coal and rock mass are improved, the precision of the evaluation result is ensured, and the evaluation result of the impact tendency of the coal and rock mass is obtained more conveniently and efficiently.

The invention discloses a frozen embryo transplantation outcome prediction method based on multi-modal data, belongs to the field of medical artificial intelligence, and is suitable for single embryo and double embryo transplantation. The method comprises the following steps: firstly, acquiring two high-resolution embryo images and corresponding clinical data of a patient, and copying the images during single embryo transplantation; the image resolution is unified by fixing the aspect ratio of an original image and properly cutting and filling. A standard picture enhancement technology is adopted for model training, and adaptability to variable imaging conditions is improved. During prediction, two preprocessed images share weights in a network with ResNet-50 as a trunk, and feature extraction is enhanced by combining a CBAM attention mechanism and a feature pyramid network. And the extracted features are averagely pooled and then directly connected with the output of a full-connection network for processing clinical data of the patient. And finally, through a two-layer full-connection network and a Sigmoid activation function, obtaining a prediction result of embryo transplantation by using binary threshold mapping.

The invention discloses an ultrasonic image few-sample target detection method and system based on a Mangbar model, and the method comprises the following steps: extracting the image features of an ultrasonic image, and generating a suggestion box; performing position coding on the picture features, obtaining new picture features by using a global information enhancement module, and generating suggestion box features in combination with the generated suggestion box; obtaining new suggestion box features by using a category relationship learning module; generating a suggestion box classification score and a coordinate prediction value; parameter updating is carried out, and if the training round reaches the maximum number of iterations, the process is ended; otherwise, repeating the steps. A Mamba model is used for obtaining global information, and the Mamba model shows deeper semantic understanding and performance enhancement potential in context learning by means of the capabilities of context modeling and long-range dependency relationship capturing.

The invention provides a medical information data query method and system based on artificial intelligence, and relates to the field of data processing.The system comprises a data acquisition module used for acquiring medical data; the data processing module is used for preprocessing the medical data; the data encryption module is used for encrypting the preprocessed medical data; the data storage module is used for storing the encrypted medical data; the data query module is used for receiving a data query request of the patient end or the medical care end, performing permission verification on the patient end or the medical care end based on the data query request, determining target medical data corresponding to the data query request through artificial intelligence after the permission verification, and feeding back the target medical data to the patient end or the medical care end; the method has the advantage of improving the intelligence of medical data query.

The invention discloses a machining product quality online detection precision evaluation method based on multi-factor fusion. The method comprises the following steps: firstly, determining machining process input factor characteristics of online detection precision evaluation and an output machining precision grade; secondly, historical samples of the factor characteristics are analyzed, an atomization sample plate set is generated, and a random fuzzy sample plate set is constructed; an online input feature sample is converted into a random fuzzy to-be-detected set, then the random fuzzy sample set is matched with the random fuzzy to-be-detected set, and reliability distribution describing the nonlinear mapping relation between the multi-environment-factor features and the detection precision is obtained. And finally, fusing the reliability distribution, determining the precision grade of online detection of the quality of the machined product according to a fusion result, and judging whether the product is qualified or not. According to the method, a complex non-linear relationship can be processed, reliability distribution related to the detection precision can be efficiently obtained, and the online detection precision can be evaluated more accurately in practical application.

The invention discloses a seed information question-answering system and method. The invention provides a variety information question-answering system. The system comprises a computing resource; the big language model calls computing resources to perform seed information question answering, the big language model is configured to learn knowledge related to the seed from a seed field knowledge source, and the big language model comprises an input acquisition module configured to be used for acquiring text information related to seed information questions from user input; the semantic understanding module is coupled to the input acquisition module and is configured to be used for converting the text information into vector information; the information reasoning module is coupled to the semantic understanding module and is configured to be used for generating an answer information vector based on knowledge related to the seed industry in combination with the vector information and the context, and the answer information vector comprises breeding decision information; and the result output module is coupled to the information reasoning module and is configured to be used for converting the answer information vector into answer text information.

The invention discloses a power transmission line reconstruction method combined with multi-parallel-line laser, and belongs to the field of power transmission line modeling, and the method comprises the steps: determining a target power transmission line region, and carrying out the three-dimensional scanning of a multi-parallel-line laser scanner, and obtaining the point cloud data of the region; shooting a plurality of groups of binocular stereo images by using a binocular camera to obtain regional image data; establishing a registration coordinate system, and registering the point cloud and the image data to obtain an initial three-dimensional model of the power transmission line; constructing a key component set, and carrying out geometric recognition and segmentation on the models to obtain a plurality of key component models; and optimizing the plurality of key component models to obtain a plurality of optimized component models, and performing model optimization on the initial three-dimensional model of the power transmission line to obtain an accurate three-dimensional model of the power transmission line. The technical problems of low precision and poor efficiency of three-dimensional model reconstruction of the power transmission line in the prior art are solved, and the technical effects of improving the three-dimensional model reconstruction precision of the power transmission line and realizing efficient and accurate three-dimensional reconstruction of the power transmission line are achieved.

The invention provides biopsy liquid and application thereof in pig blastocyst laser cutting, and belongs to the technical field of animal biology. The invention provides a biopsy solution and a preparation method thereof, the biopsy solution can be applied to pig blastocyst biopsy by laser cutting, the problem that at present, no adaptive biopsy solution exists for laser cutting of pig blastocyst is solved, and the invention further provides a method for laser cutting of pig blastocyst by using the biopsy solution. The problems of embryo cell injury, embryo development potential damage and the like possibly caused by laser cutting of the pig blastocyst in the prior art are solved. The biopsy liquid and the use method thereof can be efficiently and accurately applied to acquisition of pig blastocyst trophoblast cells, and are low in cost and good in repeatability.

The invention discloses an image reconstruction algorithm, an image processing method and device, a vehicle and a storage medium. The image processing method comprises the steps of obtaining a visible light image and an auxiliary modal image to be fused; and performing fusion reconstruction on the visible light image and the auxiliary modal image by using an image reconstruction algorithm to obtain a target image, the image reconstruction algorithm comprising a step of completing a feature extraction task of the associated visible light image by using a high-frequency attention mechanism and a Fourier convolution mechanism in the high-frequency attention block. Therefore, the high-frequency information in the visible light image with various image qualities can be rapidly and effectively extracted through the image reconstruction algorithm, so that the target image is obtained by fusing and reconstructing the visible light image and the auxiliary modal image based on the effectively extracted high-frequency information; and features in a visible light image and features in an auxiliary modal image are clearer. Therefore, it can be ensured that images with high quality can be obtained when shooting is carried out in various shooting environments.

The invention belongs to the technical field of knowledge tracking, and particularly discloses a knowledge tracking method based on various learning behaviors. The knowledge tracking method based on various learning behaviors comprises three parts of data preprocessing and vector representation coding, a multi-head self-attention mechanism fusing a forgetting behavior and a transformation relation between the behaviors, and model training and prediction. According to the invention, on the basis of various learning behavior data, a knowledge tracking problem is modeled from a whole-process learning scene of a learner, so that a conversion relation of different types of learning behaviors and a personalized learning mode of the learner can be modeled by a built knowledge tracking model of various learning behaviors; and meanwhile, a learner forgetting behavior based on learning time and repetition frequency modeling is introduced, so that the knowledge state change condition in the whole learning process of the learner is better modeled, the knowledge state information of the learner is effectively acquired from various learning behaviors, and a knowledge tracking task is completed.

The invention relates to the field of robots, and discloses a cube carrying and angle and posture adjusting robot which comprises a movable chassis; the XZ-axis linear moving platform is arranged on the moving chassis so as to move in the X-axis direction and the Z-axis direction. The Y-axis linear moving assembly is arranged on the rear portion of the X-Z-axis linear moving platform and drives the cubic object to linearly move along the Y axis. The obtaining mechanisms are arranged on the two sides of the XZ-axis linear moving platform and used for obtaining the cubic objects and placing the cubic objects in the XZ-axis linear moving platform. The posture adjusting mechanisms are arranged on the two sides of the XZ-axis linear moving platform and used for adjusting the posture of an object entering the XZ-axis linear moving platform. And the placing mechanism is arranged on the Y-axis linear moving assembly and used for obtaining the articles subjected to posture adjustment through the posture adjusting mechanism, the Y-axis linear moving assembly drives the articles to move to the designated position, and the articles are released to the designated position through the placing mechanism. According to the invention, acquisition, angle and posture adjustment, transportation and stacking of the cube can be realized.